One of the most common misconceptions in Machine Learning is that ML Engineers get a CSV dataset and they spend the majority of the time optimizing the hyperparameters of a model.

If you work in the industry, you know that’s far from the truth. ML Engineers spend most of the time planning how to construct the training set that resembles real-world data distribution for a certain problem.

When you’ve managed to construct such training set, just add a few well-crafted features and the Machine Learning model won’t have a hard time finding the decision boundary.

In this article, we’re going to go through 8 Machine Learning tips that will help you to train a model with fewer screw-ups. These tips are most useful when you need to construct the training set, e.g. you didn’t get it from Kaggle.

At the end of the article, I also share a link to the Jupyter Notebook template, which you can incorporate into your Machine Learning workflow.

#ai & machine learning #bugs #machine learning model #training